Abstract
A weight based emotion recognition system is presented to classify emotions using audio signals recorded in three south Indian languages. An audio database with containing five emotional states namely anger, surprise, disgust, happiness, and sadness is created. For subjective validation, the database is subjected to human listening test. Relevant features for recognizing emotions from speech are extracted after suitably pre-processing the samples. The classification methods, K-Nearest Neighbor, Support Vector Machine and Neural Networks are used for detection of respective emotions. For classification purpose the features are weighted so as to maximize the inter cluster separation in feature space. An inter performance comparison of the above classification methods using normal, weighted features as well as feature combinations are analyzed.
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Poorna, S.S., Anuraj, K., Nair, G.J. (2018). A Weight Based Approach for Emotion Recognition from Speech: An Analysis Using South Indian Languages. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_2
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DOI: https://doi.org/10.1007/978-981-13-1936-5_2
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